Behavioral Data Science is a new, emerging, interdisciplinary field, which combines techniques from the behavioural sciences, such as psychology, economics, sociology, and business, with computational approaches from computer science, statistics, data-centric engineering, information systems research and mathematics, all in order to better model, understand and predict behaviour in practice. This field emerges as a direct response to the need for studying behaviour “in the wild”, outside of ideal laboratory settings and controlled environments. Behavioral Data Science lies at the interface of all these disciplines (and a growing list of others) – all interested in combining deep knowledge about the questions underlying human, algorithmic, and systems behaviour with increasing quantities of data.

The kinds of questions this field engages are not only exciting and challenging, but also timely, such as: How can people’s wellbeing at scale be measured and improved using behavioural data science? How can we improve the entire supply chain in creative industries and produce movies, which viewers really want to see? How can we better understand machine behaviour and algorithmic behaviour? How can we better model social systems by mapping risk through time? How can we design and deliver personalised services ethically and responsibly? Behavioral Data Science is capable of addressing all these issues (and many more) partly because of the availability of new data sources and partly due to the emergence of new (hybrid) models, which merge behavioural science and data science models.

The main advantage of these models is that they expand machine learning techniques that are otherwise black boxes, making machine learning models transparent, tractable and explainable. For example, while a deep learning model can generate accurate prediction of why people select one coffee shop over the other, it will not tell you what exactly drives people’s preferences; whereas hybrid models, such as anthropomorphic learning, will be able to provide this insight.

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The Foundations of Behavioural Data Science Course provides an introduction to these models illustrating the behavioural methodology with case studies from Finance and FinTech industry.

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Assessment:  At the end of the course you need to complete an online test. You can do it in your own time by the specified deadline. The test is multiple choice and you need to get at least 75% of questions right to pass. Additionally, throughout the course you will be working on your Behavioural Data Science Cookbook. To pass, you would also need to submit your cookbook (your online test will have a field, where you would be able to upload your cookbook).

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Download the cookbook template HERE

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Cookbook in the PDF Format is available HERE